PeerJ Computer Science Preprints: Optimization Theory and Computationhttps://peerj.com/preprints/index.atom?journal=cs&subject=10800Optimization Theory and Computation articles published in PeerJ Computer Science PreprintsResolve the cell formation problem in a set of three manufacturing cellshttps://peerj.com/preprints/276922019-04-292019-04-29Boris Almonacid
The problem of cell formation is an NP-Hard problem, which consists of organising a group of machines and pieces in several cells. The machines are arranged in a fixed way inside the cells, and each machine has some manufacturing operation that applies in different pieces or parts. The idea of the problem is to be able to minimise the movements made by the pieces to reach the machines in the cells. For this problem, a data set has been organised using three manufacturing cells. Through the data set an experiment has been carried out that focuses on obtaining the best solution using a global search solution within 6 days for each instance. The experimental results have been able to obtain the general optimum value for a set of test instances.
The problem of cell formation is an NP-Hard problem, which consists of organising a group of machines and pieces in several cells. The machines are arranged in a fixed way inside the cells, and each machine has some manufacturing operation that applies in different pieces or parts. The idea of the problem is to be able to minimise the movements made by the pieces to reach the machines in the cells. For this problem, a data set has been organised using three manufacturing cells. Through the data set an experiment has been carried out that focuses on obtaining the best solution using a global search solution within 6 days for each instance. The experimental results have been able to obtain the general optimum value for a set of test instances.Preliminary experiments with the Andean Condor Algorithm to solve problems of Continuous Domainshttps://peerj.com/preprints/276782019-04-242019-04-24Boris L Almonacid
In this article a preliminary experiment is carried out in which a set of elements and procedures are described to be able to solve problems of continuous domains integrated in the Andean Condor Algorithm. The Andean Condor Algorithm is a metaheuristic algorithm of swarm intelligence inspired by the movement pattern of the Andean condor when searching for its food. An experiment focused on solving the problem of the function 1st De Jong's \(f(x_1 \cdots x_n) = \sum_{i=1}^n x_i^2,~ -100 \leq x_i \leq 100\). According to the results obtained, solutions have been obtained close to the overall optimum value of the problem.
In this article a preliminary experiment is carried out in which a set of elements and procedures are described to be able to solve problems of continuous domains integrated in the Andean Condor Algorithm. The Andean Condor Algorithm is a metaheuristic algorithm of swarm intelligence inspired by the movement pattern of the Andean condor when searching for its food. An experiment focused on solving the problem of the function 1st De Jong's \(f(x_1 \cdots x_n) = \sum_{i=1}^n x_i^2,~ -100 \leq x_i \leq 100\). According to the results obtained, solutions have been obtained close to the overall optimum value of the problem.IPOMOEA: Intended package orientation using multi-objective evolutionary algorithm in Rhttps://peerj.com/preprints/276562019-04-162019-04-16M A El-dosuky
Programmers’ lack of familiarity with what is available in packages may prompt them to reinvent the wheel. This is generally the case in any programming language, but it is a matter of madness with a language described as difficult even by professionals supporting it such as R. In R Cookbook, says: “But R can be frustrating. It’s not obvious how to accomplish many tasks, even simple ones.” IPOMOEA is a code that has been written to mitigate this problem. It helps R language developers determine how to perform a specific task, by automating the search in R site for all packages that are likely to contribute to the task implementation. After that, IPOMOEA determines a partial set of results to be the intended package using multi-objective evolutionary algorithm NSGA-II . Not only does it specify the intended package, but also it helps orient programmers and manage packages. Keywords:
Programmers’ lack of familiarity with what is available in packages may prompt them to reinvent the wheel. This is generally the case in any programming language, but it is a matter of madness with a language described as difficult even by professionals supporting it such as R. In R Cookbook, says: “But R can be frustrating. It’s not obvious how to accomplish many tasks, even simple ones.” IPOMOEA is a code that has been written to mitigate this problem. It helps R language developers determine how to perform a specific task, by automating the search in R site for all packages that are likely to contribute to the task implementation. After that, IPOMOEA determines a partial set of results to be the intended package using multi-objective evolutionary algorithm NSGA-II . Not only does it specify the intended package, but also it helps orient programmers and manage packages. Keywords:A local search algorithm for the constrained max cut problem on hypergraphs.https://peerj.com/preprints/274342018-12-182018-12-18Nasim SameiRoberto Solis-Oba
In the constrained max k-cut problem on hypergraphs, we are given a weighted hypergraph H=(V, E), an integer k and a set c of constraints. The goal is to divide the set V of vertices into k disjoint partitions in such a way that the sum of the weights of the hyperedges having at least two endpoints in different partitions is maximized and the partitions satisfy all the constraints in c. In this paper we present a local search algorithm for the constrained max k-cut problem on hypergraphs and show that it has approximation ratio 1-1/k for a variety of constraints c, such as for the constraints defining the max Steiner k-cut problem, the max multiway cut problem and the max k-cut problem. We also show that our local search algorithm can be used on the max k-cut problem with given sizes of parts and on the capacitated max k-cut problem, and has approximation ratio 1-|Vmax|/|V|, where |Vmax| is the cardinality of the biggest partition. In addition, we present a local search algorithm for the directed max k-cut problem that has approximation ratio (k-1)/(3k-2).
In the constrained max k-cut problem on hypergraphs, we are given a weighted hypergraph H=(V, E), an integer k and a set c of constraints. The goal is to divide the set V of vertices into k disjoint partitions in such a way that the sum of the weights of the hyperedges having at least two endpoints in different partitions is maximized and the partitions satisfy all the constraints in c. In this paper we present a local search algorithm for the constrained max k-cut problem on hypergraphs and show that it has approximation ratio 1-1/k for a variety of constraints c, such as for the constraints defining the max Steiner k-cut problem, the max multiway cut problem and the max k-cut problem. We also show that our local search algorithm can be used on the max k-cut problem with given sizes of parts and on the capacitated max k-cut problem, and has approximation ratio 1-|Vmax|/|V|, where |Vmax| is the cardinality of the biggest partition. In addition, we present a local search algorithm for the directed max k-cut problem that has approximation ratio (k-1)/(3k-2).Mixed integer nonlinear programming for three-dimensional aircraft conflict avoidancehttps://peerj.com/preprints/274102018-12-052018-12-05Junling CaiNing Zhang
The problem of aircraft conflict avoidance for Air Traffic Management systems is studied. In the scenario, aircraft are considered to fly within a shared three-dimensional airspace and not allowed to approach close less than a minimum safe separation during their flights in order to avoid various conflicts. This paper proposes a formulation of the three-dimensional conflict avoidance problem as a Mixed Integer Non-Linear Programming (MINLP) model where aircraft are allowed to change both their heading angle and velocity simultaneously to keep the separation. The validity of the proposed model is demonstrated by a comparison of the results from the MINLP model and the previous conflict avoidance models with one maneuver of the heading angle or the velocity. The numerical studies show that the MINLP model improves the efficiency of computation and maintain the safety of flights even by using a standard global optimization solver
The problem of aircraft conflict avoidance for Air Traffic Management systems is studied. In the scenario, aircraft are considered to fly within a shared three-dimensional airspace and not allowed to approach close less than a minimum safe separation during their flights in order to avoid various conflicts. This paper proposes a formulation of the three-dimensional conflict avoidance problem as a Mixed Integer Non-Linear Programming (MINLP) model where aircraft are allowed to change both their heading angle and velocity simultaneously to keep the separation. The validity of the proposed model is demonstrated by a comparison of the results from the MINLP model and the previous conflict avoidance models with one maneuver of the heading angle or the velocity. The numerical studies show that the MINLP model improves the efficiency of computation and maintain the safety of flights even by using a standard global optimization solverNovel approach for solving integer equal flow problemhttps://peerj.com/preprints/272642018-10-082018-10-08Swapnil KumarSasikanth Goteti
In this article we consider a certain sub class of Integer Equal Flow problem, which are known NP hard. Currently there exist no direct solutions for the same. It is a common problem in various inventory management systems. Here we discuss a local minima solution which uses projection of the convexspaces to resolve the equal flows and turn the problem into a known linear integer programming or constraint satisfaction problem which have reasonable known solutions and can be effectively solved using simplex or other standard optimization strategies
In this article we consider a certain sub class of Integer Equal Flow problem, which are known NP hard. Currently there exist no direct solutions for the same. It is a common problem in various inventory management systems. Here we discuss a local minima solution which uses projection of the convexspaces to resolve the equal flows and turn the problem into a known linear integer programming or constraint satisfaction problem which have reasonable known solutions and can be effectively solved using simplex or other standard optimization strategiesResolving the optimal selection of a natural reserve using the particle swarm optimisation by applying transfer functionshttps://peerj.com/preprints/269412018-05-292018-05-29Boris Almonacid
The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.
The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.GenHap: A novel computational method based on genetic algorithms for haplotype assemblyhttps://peerj.com/preprints/32462017-09-122017-09-12Andrea TangherloniSimone SpolaorLeonardo RundoMarco S NobileIvan MerelliPaolo CazzanigaDaniela BesozziGiancarlo MauriPietro Liò
The process of inferring a full haplotype of a cell is known as haplotyping, which consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. In this work, we propose a novel computational method for haplotype assembly based on Genetic Algorithms (GAs), named GenHap. Our approach could efficiently solve large instances of the weighted Minimum Error Correction (wMEC) problem, yielding optimal solutions by means of a global search process. wMEC consists in computing the two haplotypes that partition the sequencing reads into two unambiguous sets with the least number of corrections to the SNP values. Since wMEC was proven to be an NP-hard problem, we tackle this problem exploiting GAs, a population-based optimization strategy that mimics Darwinian processes. In GAs, a population composed of randomly generated individuals undergoes a selection mechanism and is modified by genetic operators. Based on a quality measure (i.e., the fitness value), inspired by Darwin’s “survival of the fittest” laws, each individual is involved in a selection process.
Our preliminary experimental results show that GenHap is able to achieve correct solutions in short running times. Moreover, this approach can be used to compute haplotypes in organisms with different ploidity. The proposed evolutionary technique has the advantage that it could be formulated and extended using a multi-objective fitness function taking into account additional insights, such as the methylation patterns of the different chromosomes or the gene proximity in maps achieved through Chromosome Conformation Capture (3C) experiments.
The process of inferring a full haplotype of a cell is known as haplotyping, which consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. In this work, we propose a novel computational method for haplotype assembly based on Genetic Algorithms (GAs), named GenHap. Our approach could efficiently solve large instances of the weighted Minimum Error Correction (wMEC) problem, yielding optimal solutions by means of a global search process. wMEC consists in computing the two haplotypes that partition the sequencing reads into two unambiguous sets with the least number of corrections to the SNP values. Since wMEC was proven to be an NP-hard problem, we tackle this problem exploiting GAs, a population-based optimization strategy that mimics Darwinian processes. In GAs, a population composedof randomly generated individuals undergoes a selection mechanism and is modified by genetic operators. Based on a quality measure (i.e., the fitness value), inspired by Darwin’s “survival of the fittest” laws, each individual is involved in a selection process.Our preliminary experimental results show that GenHap is able to achieve correct solutions in short running times. Moreover, this approach can be used to compute haplotypes in organisms with different ploidity. The proposed evolutionary technique has the advantage that it could be formulated and extended using a multi-objective fitness function taking into account additional insights, such as the methylation patterns of the different chromosomes or the gene proximity in maps achieved through Chromosome Conformation Capture (3C) experiments.The solution of large-scale Minimum Cost SAT Problem as a tool for data analysis in bioinformaticshttps://peerj.com/preprints/26352016-12-122016-12-12Giovanni FeliciDaniele FeronePaola FestaAntonio NapoletanoTommaso Pastore
Data mining is one of the main activities in bioinformatics, specifically to extract knowledge from massive data sets related with gene expression measurement, CNV, DNA strings, and others. A long array of methods are used to perform such task, ranging from the more established parametric statistical analysis to non parametric techniques, to classification methods that have been developed in knowledge engineering and artificial intelligence. In this paper, we consider a method for extracting logic formulas from data that relies on a large body of literature in integer and logic optimization, originally presented in [1], that has been largely and successfully applied to different problems in bioinformatics ([2], [3], [4], [5], [6]). Such method is based on the iterative solution of Minimum Cost SAT Problems and is able to extract logic formulas in DNF form that possess interesting features for their interpretation. While leaving the discussion of the main features and motivations of this approach to the related literature, in this talk we focus on the problem of solving efficiently very large scale instances of this well known logic programming problem and propose a new GRASP approach that, being able to exploit the specific structure of the problem, largely outperforms other established solvers for the same problem.
References
[1] G. Felici, K. Truemper. A Minsat Approach for Learning in Logic Domains, INFORMS Journal on Computing 14(1): 20-36 (2002).
[2] P. Bertolazzi, G. Felici, E. Weitschek. Learning to classify species with barcodes, BMC Bioinformatics, 10:1-12 (2009).
[3] M. Arisi, R. D’Onofrio, A. Brandi, S. Felsani, G. Capsoni, G. Drovandi, G. Felici, E. Weitschek, P. Bertolazzi, A. Cattaneo. Gene Expression Biomarkers in the Brain of a Mouse Model for Alzheimer’s Disease: Mining of Microarray Data by Logic Classification and Feature Selection. Journal of Alzheimer's Disease, 24(4) 721-738 (2011).
[4] E. Weitschek, A. Lo Presti, G. Drovandi, G. Felici, M. Ciccozzi, M. Ciotti, P. Bertolazzi. Human polyomaviruses identification by logic mining techniques. BMC Virology Journal, 9:58 (2012).
[5] E. Weitschek, G. Fiscon, G. Felici. Supervised DNA Barcodes species classification: analysis, comparisons and results, BMC BioData Mining, 7:4 (2014).
[6] P. Bertolazzi, G. Felici, P. Festa, G. Fiscon, E. Weitschek. Integer Programming models for Feature Selection: new extensions and a randomized solution algorithm, European Journal of Operational Research, 250-389–399, 250 (2016).
Data mining is one of the main activities in bioinformatics, specifically to extract knowledge from massive data sets related with gene expression measurement, CNV, DNA strings, and others. A long array of methods are used to perform such task, ranging from the more established parametric statistical analysis to non parametric techniques, to classification methods that have been developed in knowledge engineering and artificial intelligence. In this paper, we consider a method for extracting logic formulas from data that relies on a large body of literature in integer and logic optimization, originally presented in [1], that has been largely and successfully applied to different problems in bioinformatics ([2], [3], [4], [5], [6]). Such method is based on the iterative solution of Minimum Cost SAT Problems and is able to extract logic formulas in DNF form that possess interesting features for their interpretation. While leaving the discussion of the main features and motivations of this approach to the related literature, in this talk we focus on the problem of solving efficiently very large scale instances of this well known logic programming problem and propose a new GRASP approach that, being able to exploit the specific structure of the problem, largely outperforms other established solvers for the same problem.References[1] G. Felici, K. Truemper. A Minsat Approach for Learning in Logic Domains, INFORMS Journal on Computing 14(1): 20-36 (2002).[2] P. Bertolazzi, G. Felici, E. Weitschek. Learning to classify species with barcodes, BMC Bioinformatics, 10:1-12 (2009).[3] M. Arisi, R. D’Onofrio, A. Brandi, S. Felsani, G. Capsoni, G. Drovandi, G. Felici, E. Weitschek, P. Bertolazzi, A. Cattaneo. Gene Expression Biomarkers in the Brain of a Mouse Model for Alzheimer’s Disease: Mining of Microarray Data by Logic Classification and Feature Selection. Journal of Alzheimer's Disease, 24(4) 721-738 (2011).[4] E. Weitschek, A. Lo Presti, G. Drovandi, G. Felici, M. Ciccozzi, M. Ciotti, P. Bertolazzi. Human polyomaviruses identification by logic mining techniques. BMC Virology Journal, 9:58 (2012).[5] E. Weitschek, G. Fiscon, G. Felici. Supervised DNA Barcodes species classification: analysis, comparisons and results, BMC BioData Mining, 7:4 (2014).[6] P. Bertolazzi, G. Felici, P. Festa, G. Fiscon, E. Weitschek. Integer Programming models for Feature Selection: new extensions and a randomized solution algorithm, European Journal of Operational Research, 250-389–399, 250 (2016).Engineering permanence in finite systemshttps://peerj.com/preprints/24542016-11-122016-11-12Daniel Bilar
The man-machine integration era (MMIE) is marked by sensor ubiquity, whose readings map human beings to finite numbers. These numbers processed by continuously changing, optimizing/learning, finite precision, closed loop, distributed systems are used to drive decisions such as insurance rates, prison sentencing, health care allocations and probation guidelines. Optimization and system parameter tuning is increasingly left to machine learning and applied AI. One challenge we face is thus: Ensuring the indelibility, the permanence, the infinite value of human beings as optimization-resistant invariants in such system environments. In this challenge paper, we propose developing safeguards, specifically working towards a 'deontological imprimatur' architecture embedding resilient representations of human beings.
The man-machine integration era (MMIE) is marked by sensor ubiquity, whose readings map human beings to finite numbers. These numbers processed by continuously changing, optimizing/learning, finite precision, closed loop, distributed systems are used to drive decisions such as insurance rates, prison sentencing, health care allocations and probation guidelines. Optimization and system parameter tuning is increasingly left to machine learning and applied AI. One challenge we face is thus: Ensuring the indelibility, the permanence, the infinite value of human beings as optimization-resistant invariants in such system environments. In this challenge paper, we propose developing safeguards, specifically working towards a 'deontological imprimatur' architecture embedding resilient representations of human beings.